An omnibus permutation test on ensembles of two-locus analyses can detect pure epistasis and genetic heterogeneity in genome-wide association studies.

This article presents the ability of an omnibus permutation test on ensembles of two-locus analyses (2LOmb) to detect pure epistasis in the presence of genetic heterogeneity. The performance of 2LOmb is evaluated in various simulation scenarios covering two independent causes of complex disease where each cause is governed by a purely epistatic interaction. Different scenarios are set up by varying the number of available single nucleotide polymorphisms (SNPs) in data, number of causative SNPs and ratio of case samples from two affected groups. The simulation results indicate that 2LOmb outperforms multifactor dimensionality reduction (MDR) and random forest (RF) techniques in terms of a low number of output SNPs and a high number of correctly-identified causative SNPs. Moreover, 2LOmb is capable of identifying the number of independent interactions in tractable computational time and can be used in genome-wide association studies. 2LOmb is subsequently applied to a type 1 diabetes mellitus (T1D) data set, which is collected from a UK population by the Wellcome Trust Case Control Consortium (WTCCC). After screening for SNPs that locate within or near genes and exhibit no marginal single-locus effects, the T1D data set is reduced to 95,991 SNPs from 12,146 genes. The 2LOmb search in the reduced T1D data set reveals that 12 SNPs, which can be divided into two independent sets, are associated with the disease. The first SNP set consists of three SNPs from MUC21 (mucin 21, cell surface associated), three SNPs from MUC22 (mucin 22), two SNPs from PSORS1C1 (psoriasis susceptibility 1 candidate 1) and one SNP from TCF19 (transcription factor 19). A four-locus interaction between these four genes is also detected. The second SNP set consists of three SNPs from ATAD1 (ATPase family, AAA domain containing 1). Overall, the findings indicate the detection of pure epistasis in the presence of genetic heterogeneity and provide an alternative explanation for the aetiology of T1D in the UK population.

Small Ancestry Informative Marker panels for complete classification between the original four HapMap populations.

A protocol for the identification of Ancestry Informative Markers (AIMs) from genome-wide Single Nucleotide Polymorphism (SNP) data is proposed. The protocol consists of three main steps: identification of potential positive selection regions via F(ST) extremity measurement, SNP screening via two-stage attribute selection and classification model construction using a Naïve Bayes classifier. The two-stage attribute selection is composed of a newly developed round robin Symmetrical Uncertainty (SU) ranking technique and a wrapper embedded with a Naïve Bayes classifier. The protocol has been applied to the HapMap Phase II data. Two AIM panels, which consist of 10 and 16 SNPs that lead to complete classification between CEU, CHB, JPT and YRI populations, are identified. Moreover, the panels are at least four times smaller than those reported in previous studies. The results suggest that the protocol could be useful in a scenario involving a larger number of populations.